For each label I am currently:
- bootstrapping the model with patterns
prefer_high_scoressorter and annotating until the progress bar shows around 90 % (usually I need something over 1000 examples)
textcat.batch-trainthat typically achieves around 75 % F-score
At this point, I would like to boost the preformance by adding additional examples using textcat.teach and
prefer_uncertain sorter. (Hopefully, this workflow is sensible, or should I rather be focusing at hyperparameter tuning?) However, when I start
textcat.teach again, based on the progress bar, it seems that the model in the loop is only trained based on the actual session.
Is there any way how to initialise the model in the loop based on all the examples in the db?